C/C++与Matlab混合编程

     Matlab 拥有丰富的功能，编程简单。不过，有些情况下，Matlab程序的执行速度比较慢。C/C++编译执行的程序速度比较快，编程难度上比Matlab要高一些。因此存在一种方案，就是使用Matlab实现我们的实验程序，用C/C++来实现Matlab程序中比较耗时的部分，从Matlab程序中调用C/C++的程序以实现加速。

Visual C++ 2015

1：配置环境

1.1：在vc++目录中

包含目录：(1)：生成的mydll.h所在目录。

          (2)：matlab 内的include目录。

Include files

D:Program FilesMATLABR2012aexterninclude

库目录：(1)：mydll.lib所在目录。

        (2)：matlab的lib目录。

Library files

D:New Project手写体数字识别QpPrjdistrib

D:Program FilesMATLABR2012aexternlibwin64microsoft

1.2：在连接器-》输入-》附加依赖项  

mclmcrrt.lib

libmx.lib

libmat.lib

mclmcr.lib

QpPrj.lib

将编译好的dll复制到VC工程的Debug或者Release目录下，以使得dll可以被找到。还要把编译生成的QpPrj.h文件拷贝到VC工程里

 

代码实现：

#include <iostream>
#pragma comment(lib,"QpPrj.lib")
#include "QpPrj.h"
#include "mclmcr.h"   
#include "matrix.h"   
#include "mclcppclass.h"  

using namespace std;

int main(int argc, char* argv[])
{
	// 初始化  
	if (!QpPrjInitialize())
	{
		printf("Could not initialize !");
		return -1;
	}

	// 1.调用MyAdd
	double a = 6;
	double b = 9;
	double c;

	// 为变量分配内存空间   
	mwArray mwA(1, 1, mxDOUBLE_CLASS); // 1，1表示矩阵的大小（所有maltab只有一种变量，就是矩阵，为了和Cpp变量接轨，设置成1*1的矩阵，mxDOUBLE_CLASS表示变量的精度）   
	mwArray mwB(1, 1, mxDOUBLE_CLASS);
	mwArray mwC(1, 1, mxDOUBLE_CLASS);

	// set data，调用类里面的SetData函数给类赋值   
	mwA.SetData(&a, 1);
	mwB.SetData(&b, 1);

	// using my add，掉我自己写的函数
	// 调用示例: extern LIB_QpPrj_CPP_API void MW_CALL_CONV MyAdd(int nargout, mwArray& c, const mwArray& a, const mwArray& b);
	MyAdd(1, mwC, mwA, mwB);

	// get data，调用类里面的Get函数获取取函数返回值   
	c = mwC.Get(1, 1);
	printf("c is %f
", c);

	// 2.调用TestChar
	// extern LIB_QpPrj_CPP_API void MW_CALL_CONV TestChar(int nargout, mwArray& result, const mwArray& char0)

	double d = 4;
	double e;

	// 为变量分配内存空间   
	mwArray mwInput(1, 1, mxDOUBLE_CLASS);
	mwArray mwOutput(1, 1, mxDOUBLE_CLASS);

	mwInput.SetData(&d, 1);

	TestChar(1, mwOutput, mwInput);
	e = mwOutput.Get(1, 1);
	printf("e is %f
", e);

	char training_result_path[] = "D:\New Project\手写体数字识别";
	char digital_img_path[] = "D:\hh\t_4.jpg";
	char training_result_file[] = "training_result_200_trees";

	double f;

	cout << training_result_path << endl;
	cout << digital_img_path << endl;
	cout << training_result_file << endl;

	// 为变量分配内存空间
	mwArray recognition_result(1, 1, mxDOUBLE_CLASS); 

	// 调用示例:extern LIB_QpPrj_CPP_API void MW_CALL_CONV digital_recogn_9(int nargout, mwArray& recognition_result, const mwArray& training_result_path, const mwArray& digital_img_path, const mwArray& training_result_file);
	digital_recogn_9(1, recognition_result, training_result_path, digital_img_path, training_result_file);

	f = recognition_result.Get(1, 1);

	// 终止调用的程序
	QpPrjTerminate();

	// terminate MCR
	mclTerminateApplication();

	return 0;


}

　　

... MWMCR::EvaluateFunction error ...

Error using predict (line 85)

Systems of uint32 class cannot be used with the "predict" command. Convert the system to an identified model first, such as by using the "idss" command.

matlab代码

digital_recogn_9.m

function   [recognition_result]

= digital_recogn_9( training_result_path , digital_img_path , training_result_file )

1 training_result_path 

训练库路径   ' D:New Project手写体数字识别'

2 digital_img_path 

传入的图片路径  ' D:hh _4.jpg '

3 training_result_file 

训练库文件名   ' training_result_200_trees '

调用示例：

digital_recogn_9('D:New Project手写体数字识别','D:hh _4.jpg','training_result_200_trees')

function   [recognition_result] = digital_recogn_9( training_result_path , digital_img_path , training_result_file )

 

input_number = imread(digital_img_path) ;

 

%%%%%%%%%%%%     image trsformation

input_number = 255 - rgb2gray(input_number) ;

threshold_noise = 35 ;

 

for ii = 1:size(input_number,1)

        for jj = 1:size(input_number,2)

                if   input_number( ii , jj ) < threshold_noise

                        input_number( ii , jj ) = 0;

                end    

        end

end

 

%%%%%%%%%%%%%%%%%%%%%%%%

projecting_width = sum(input_number, 1);

projecting_height = sum(input_number, 2);

 

%%%%%%%  cut off the boundary

boundary_width = zeros( size( projecting_width ) );

boundary_height = zeros( size( projecting_height ) );

offset_width = 3;

offset_height = 3;

 

aa = size( boundary_width , 2) - offset_width + 1  ;

boundary_width ( 1 , offset_width : aa ) = 1 ;

 

bb =   size( boundary_height , 1) - offset_height  + 1  ;

boundary_height ( offset_height : bb , 1 ) = 1 ;

 

projecting_width = projecting_width .* boundary_width ;

projecting_height = projecting_height .* boundary_height ;

 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

threshold_width = max ( projecting_width ) / size(projecting_width , 2 ) ;

threshold_height = max ( projecting_height ) / size(projecting_height , 1 ) ;

 

s1 = ( projecting_width > threshold_width ) ;

new_width = sum(s1) ;

 

s2 = ( projecting_height > threshold_height ) ;

new_height = sum(s2) ;

 

trimming_img = zeros( new_height , new_width );

trimming_img = uint8(trimming_img) ;

counter_height =1 ;

for ii=1 : (size( input_number , 1 ) - 1)

        %%%%%%%   select qualified rows

        if s2( ii , 1 )==true

            counter_height  = counter_height +1 ;

        end

       

        %%%%%%%   select qualified columns

        counter_width =1 ;

        for jj=1 : (size( input_number , 2 ) - 1)

                if s1( 1 , jj )==true

                    counter_width  = counter_width +1 ;

                end

            

                %%%%% copy pixels to new image

                if s2( ii , 1 ) == true  ||   s1( 1 , jj ) == true      

                        s3 = input_number( ii , jj );

                        trimming_img( counter_height ,  counter_width ) = s3;      

                end 

        end

end

 

%%%%%%%%%% flatten the image

edge_length =16 ;  %% 16*16=256

trimming_img = imresize( trimming_img , [edge_length , edge_length] ) ;

%% 暂时注释 掉

%%imshow( trimming_img ) ;

 

flatten_img = reshape( trimming_img , 1 , 256 ) ;

 

%%%%%%%%%%%%%%%%%%%%%

cd(  training_result_path )

%% 加上分号,训练库文件名 ' training_result_200_trees '

training_result = load( training_result_file );

Xtest = training_result.Xtest ;

mdl = training_result.mdl ;

 

%%%%%%%%%%%%%%%%%%%%%

image_set = zeros( size(Xtest) ) ;

image_set  = uint8( image_set  );

 

for ii=1:size(Xtest, 1)

        image_set( ii ,  : ) = flatten_img(: , :);

end

 

%%%%     Train and Predict Using a Single Classification Tree

 

Xtest = double( image_set );

%%ypred = predict(mdl, Xtest);

ypred = predict(mdl, Xtest);

 

% % Confmat_bag = confusionmat(Ytest,ypred);

 

%% recognition_result = ypred(1,1);

 

recognition_result = ypred(1,1);

 

end

 

MyAdd.m

function [c] = MyAdd(a, b)

%UNTITLED Summary of this function goes here

%   Detailed explanation goes here

c = a + b;

 

end

 

MyChar.m

function [result] = MyChar(str)

%UNTITLED2 Summary of this function goes here

%   Detailed explanation goes here

result = str;

 

 

end